library(Covid19Wastewater)
library(dplyr)
library(ggplot2)
library(tidyr)
data("HFGWaste_data", package = "Covid19Wastewater")
#crazy agressive method
hfg_outlier_detection <- function(small_vec){
upbound_vec <- quantile(small_vec, probs = .7, na.rm = TRUE)
retVec = ifelse(small_vec > upbound_vec,
NA, small_vec)
retVec = ifelse(retVec == 0, NA, retVec)
return(retVec)
}
hfg_waste_filt_df <- HFGWaste_data%>%
select(site, date, Filter, Well, N1, N2, PMMOV, BCoV, HF183, CrP)%>%
group_by(date,site)%>%
mutate(across(N1:CrP, hfg_outlier_detection))
trend_df <- hfg_waste_filt_df%>%
group_by(site)%>%
group_split()%>%
lapply(loessSmoothMod, InVar = "N1", OutVar = "N1_Trend")%>%
lapply(loessSmoothMod, InVar = "N2", OutVar = "N2_Trend")%>%
lapply(loessSmoothMod, InVar = "PMMOV", OutVar = "PMMOV_Trend")%>%
lapply(loessSmoothMod, InVar = "HF183", OutVar = "HF183_Trend")%>%
lapply(loessSmoothMod, InVar = "CrP", OutVar = "CrP_Trend")%>%
bind_rows()%>%
mutate(
N1_Diff = N1_Trend - N1,
N2_Diff = N2_Trend - N2,
PMMOV_Diff = PMMOV_Trend - PMMOV,
HF183_Diff = HF183_Trend - HF183,
CrP_Diff = CrP_Trend - CrP)%>%
select(date, site, N1_Trend:CrP_Diff)
library(ggcorrplot)
trend_df%>%
select(N1_Trend:CrP_Diff)%>%
cor(use = "complete.obs")%>%
ggcorrplot()

library(GGally)
trend_df%>%
ggpairs(aes(colour = site, alpha = 0.4))

log_trend_df <- hfg_waste_filt_df%>%
mutate(log_N1 = log(N1),
log_N2 = log(N2),
log_PMMOV = log(PMMOV),
log_HF183 = log(HF183),
log_CrP = log(CrP))%>%
group_by(site)%>%
group_split()%>%
lapply(loessSmoothMod, InVar = "log_N1", OutVar = "log_N1_Trend")%>%
lapply(loessSmoothMod, InVar = "log_N2", OutVar = "log_N2_Trend")%>%
lapply(loessSmoothMod, InVar = "log_PMMOV", OutVar = "log_PMMOV_Trend")%>%
lapply(loessSmoothMod, InVar = "log_HF183", OutVar = "log_HF183_Trend")%>%
lapply(loessSmoothMod, InVar = "log_CrP", OutVar = "log_CrP_Trend")%>%
bind_rows()%>%
mutate(
N1_Diff = log_N1_Trend - log_N1,
N2_Diff = log_N2_Trend - log_N2,
PMMOV_Diff = log_PMMOV_Trend - log_PMMOV,
HF183_Diff = log_HF183_Trend - log_HF183,
CrP_Diff = log_CrP_Trend - log_CrP)%>%
select(site, date, log_N1_Trend:CrP_Diff)
log_trend_df%>%
select(log_N1_Trend:CrP_Diff)%>%
cor(use = "complete.obs")%>%
ggcorrplot()

library(GGally)
log_trend_df%>%
ggpairs(aes(colour = site, alpha = 0.4))
